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Seasonality and cycles in tourism demand—redux

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  • Vatsa, Puneet

Abstract

Analyzing tourism demand cycles is of strong interest to researchers and forecasters. However, often the preponderance of seasonality encumbers the derivation of cycles that are free of seasonal patterns. Using recent advances in time-series econometrics, we offer a solution. We employ two methods that produce cycles that are robust to seasonal properties of the data; cycles from seasonally adjusted and unadjusted data are virtually indistinguishable.

Suggested Citation

  • Vatsa, Puneet, 2021. "Seasonality and cycles in tourism demand—redux," Annals of Tourism Research, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:anture:v:90:y:2021:i:c:s0160738320302498
    DOI: 10.1016/j.annals.2020.103105
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    References listed on IDEAS

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    1. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
    2. Fangdong Cao & Zhenfang Huang & Cheng Jin & Min Xu, 2016. "Influence of Chinese economic fluctuations on tourism efficiency in national scenic areas," Tourism Economics, , vol. 22(5), pages 884-907, October.
    3. Vatsa, Puneet, 2020. "Comovement amongst the demand for New Zealand tourism," Annals of Tourism Research, Elsevier, vol. 83(C).
    4. James D. Hamilton, 2018. "Why You Should Never Use the Hodrick-Prescott Filter," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 831-843, December.
    5. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    6. Chan, Felix & Lim, Christine, 2011. "Spectral analysis of seasonality in tourism demand," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1409-1418.
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